RedditPicks – get most recommended products from Reddit community

Hacker News

Hi HackerNews Community! I wanted to share a side project I've been working on that stemmed from a personal frustration. Like many of you, I often find myself searching online for product recommendations, like the best iPhone 15 case or the ideal running shoes for beginners. However, I consistently run into the issue of Google delivering click-bait web pages that don't really answer my questions. To tackle this, I've built a simple yet effective solution. Essentially the tool aggregates and analyzes relevant Reddit posts to summarize the most recommended products from the community. The process works offline, which means there's a slight delay in generating a full report. However, I'm working on enhancing the speed and efficiency. I personally find this tool very useful that saves me tons of time on search for products. Please let me know if you have any suggestions, any feedback would be highly appreciated.

Visit product View on Hacker NewsDec 15, 2023

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

83%83% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Strong signals: google, new · Missing: mac, agents, macos

75%75% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

53%53% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

50%50% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: personal, google · Missing: mobile apps, ios, entrepreneurs

49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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